arxiv_ml 92% Match Research Paper GPU Architects,ML Engineers,HPC Researchers,AI Hardware Developers 17 hours ago

Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects

large-language-models › model-architecture

📄 Abstract

Abstract: The rise of disaggregated AI GPUs has exposed a critical bottleneck in large-scale attention workloads: non-uniform memory access (NUMA). As multi-chiplet designs become the norm for scaling compute capabilities, memory latency and bandwidth vary sharply across compute regions, undermining the performance of traditional GPU kernel scheduling strategies that assume uniform memory access. We identify how these NUMA effects distort locality in multi-head attention (MHA) and present Swizzled Head-first Mapping, a spatially-aware scheduling strategy that aligns attention heads with GPU NUMA domains to exploit intra-chiplet cache reuse. On AMD's MI300X architecture, our method achieves up to 50% higher performance over state-of-the-art attention algorithms using conventional scheduling techniques and sustains consistently high L2 cache hit rates of 80-97%. These results demonstrate that NUMA-aware scheduling is now fundamental to achieving full efficiency on next-generation disaggregated GPUs, offering a path forward for scalable AI training and inference.

Key Contributions

This paper addresses the critical bottleneck of Non-Uniform Memory Access (NUMA) in disaggregated AI GPUs for large-scale attention workloads. It introduces 'Swizzled Head-first Mapping,' a spatially-aware scheduling strategy that aligns attention heads with GPU NUMA domains to exploit cache reuse, achieving up to 50% higher performance on AMD MI300X.

Business Value

Significantly improves the efficiency and performance of AI hardware, enabling faster training and inference for large models, and reducing operational costs for AI deployments.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, specifically demonstrated on AMD MI300X architecture, indicating practical applicability for next-generation hardware.

Limitations Addressed

Addresses the performance degradation in large-scale attention workloads caused by Non-Uniform Memory Access (NUMA) effects in modern disaggregated GPU architectures, which traditional uniform memory access assumptions fail to handle.

Performance Gains

Up to 50% higher performance over state-of-the-art attention algorithms; sustains 80-97% L2 cache hit rates.

Technical Tags

GPU ArchitectureNUMA EffectsAttention MechanismsMulti-Head Attention (MHA)Scheduling StrategyMemory LatencyBandwidthCache ReuseAMD MI300XSpatial Locality

Research Topics

GPU ComputingHardware-Accelerated AILarge Language ModelsSystem ArchitecturePerformance Optimization

Methods & Architectures

Swizzled Head-first MappingNUMA-aware schedulingExploiting intra-chiplet cache reuse Attention MechanismsMulti-Head Attention (MHA)

Applications & Tasks

High-Performance Computing AI Hardware Large-Scale AI Non-Uniform Memory Access (NUMA)Memory Latency BottlenecksGPU Performance Degradation Optimizing Attention on GPUsMitigating NUMA Effects in AI Workloads

Datasets & Benchmarks

Benchmarks

Up to 50% higher performance over state-of-the-art attention algorithms on AMD MI300X • Sustains 80-97% L2 cache hit rates

PerformanceL2 cache hit rates

Related Fields

Computer ArchitectureGPU ComputingMachine Learning SystemsHardware-Software Co-design

Keywords

GPUNUMAAttentionMHASchedulingPerformanceAMD MI300XCacheMemory LatencyAI HardwareLarge Language ModelsOptimization

Academic Context

#GPU Computing#Hardware-Accelerated AI#Large Language Models#System Architecture#Performance Optimization

Companies & Organizations

Companies Mentioned

AMD

Commercial Potential

Potential Products

Optimized GPU driversAI accelerator hardware with NUMA-aware designPerformance tuning tools for AI workloads

Target Industries

SemiconductorAI HardwareCloud ComputingTechnology

Use Case Examples

Accelerating transformer-based model trainingImproving inference speed for large AI models on specialized hardware

Competitive Edge

Provides a hardware-aware scheduling solution that directly tackles NUMA effects, offering a performance advantage over generic scheduling strategies for attention mechanisms.

Market Opportunity

The market for AI accelerators and high-performance computing is substantial and growing.

Revenue Models

Licensing of IP to hardware manufacturersperformance optimization services.

Resource Requirements

Compute Needs

Requires advanced GPU hardware with NUMA architecture, specifically demonstrated on AMD MI300X.

Data Requirements

Large datasets for training attention-based models.

Deployment Constraints

Hardware-specific optimization, primarily targeting architectures with NUMA effects.

Scalability

Addresses scalability challenges by optimizing performance on next-generation disaggregated GPUs.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for hardware integration and driver support.

Patent Potential

High, due to novel scheduling strategy and hardware-software co-design aspects.

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